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Название Coursera - Introduction to Data Science (2013)
Тип
Размер 3.88Гб

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000_appetite_whetting_1.pdf 1.00Мб
000b_appetite_whetting_2.pdf 1.14Мб
001_context.pdf 429.57Кб
002_dimensions.pdf 281.77Кб
003_this_course_1.pdf 704.24Кб
004_this_course_2.pdf 382.94Кб
005_escience.pdf 2.58Мб
006_big_data.pdf 1.26Мб
007_logistics.pdf 345.23Кб
008_data_models.pdf 620.24Кб
009_relational_motivation.pdf 294.37Кб
010_relational_algebra_intro.pdf 156.73Кб
011.5_relational_algebra_union_diff_select.pdf 364.37Кб
011.6_relational_algebra_project_cross_equijoin.pdf 380.76Кб
011.7_theta_join.pdf 492.09Кб
011.8_interpreting_complicated_sql.pdf 577.04Кб
011.9_user_defined_functions.pdf 61.58Кб
012_physical_optimization.pdf 427.16Кб
013_declarative_languages.pdf 661.33Кб
014_logical_data_independence.pdf 468.99Кб
015_scalability.pdf 223.89Кб
016_parallel_thinking.pdf 3.11Мб
017_map_reduce_abstraction.pdf 2.18Мб
018_map_reduce_pseudocode.pdf 94.11Кб
019_map_reduce_text_examples.pdf 337.57Кб
020_map_reduce_join_social_examples.pdf 326.08Кб
021_map_reduce_matrix_multiply.pdf 318.03Кб
022_map_reduce_implementation_overview.pdf 500.90Кб
023_parallel_databases.pdf 385.47Кб
024_mapreduce_and_databases.pdf 94.87Кб
025_mapreduce_and_databases_experiments.pdf 730.08Кб
026_nosql_intro.pdf 525.15Кб
027_eventual_consistency.pdf 717.72Кб
028_memcached.pdf 585.81Кб
029_dynamo.pdf 466.19Кб
030_couchdb.pdf 562.20Кб
031_bigtable.pdf 483.18Кб
032_other_google_systems.pdf 728.87Кб
033_nosql_response.pdf 746.59Кб
034_pig_intro.pdf 527.08Кб
035_pig_load_filter_group_foreach.pdf 191.03Кб
036_cogroup_join.pdf 233.74Кб
037_pig_evaluation.pdf 336.05Кб
038_stats_intro.pdf 148.64Кб
039_publication_bias.pdf 408.27Кб
040_effect_size_meta_analysis_heteroskedasticity.pdf 568.00Кб
041_fraud_benfords_law.pdf 1.22Мб
042_multiple_hypothesis_testing_CORRECTED.pdf 467.45Кб
043_recap_and_big_data.pdf 659.37Кб
044_intro_bayesian.pdf 103.51Кб
045_bayes_rule.pdf 409.41Кб
047_overview_machine_learning.pdf 328.51Кб
048_intro_machine_learning_2.pdf 92.96Кб
049_rules_1.pdf 484.23Кб
050_rules_2.pdf 278.50Кб
051_intro_trees.pdf 476.63Кб
052_information_gain.pdf 418.71Кб
053_overfitting.pdf 346.55Кб
054_evaluation_thresholds.pdf 270.59Кб
055_bootstrap.pdf 284.71Кб
056_ensembles_and_boosting.pdf 242.18Кб
057_random_forests.pdf 87.54Кб
058_nearest_neighbor.pdf 187.86Кб
059_gradient_descent_part_1.pdf 1.36Мб
060_gradient_descent_part_2.pdf 595.09Кб
061_intuition_logistic_regression_svms.pdf 640.32Кб
062_intuition_regularization.pdf 490.13Кб
063_stochastic_gradient_descent.pdf 490.33Кб
064_unsupervised_learning_copy.pdf 550.94Кб
065_kmeans.pdf 181.16Кб
066_dbscan.pdf 636.50Кб
077b_graph_histograms.pdf 481.81Кб
078_structural_analysis_tasks.pdf 398.83Кб
079_pagerank.pdf 278.59Кб
080_traversal_tasks.pdf 399.97Кб
081_pattern_matching.pdf 467.50Кб
082_relational_algebra_for_graph_tasks.pdf 91.43Кб
083_prism_example.pdf 147.04Кб
084_evaluating_recursive_programs.pdf 576.99Кб
085_optimizing_recursive_programs_in_mr.pdf 437.88Кб
086_graph_representations.pdf 423.76Кб
087_pagerank_mapreduce_pregel.pdf 803.64Кб
1_instructions.pdf 260.96Кб
10 - 1 - 01 Guest Segment Aaron Kimball Wibidata.mp4 21.26Мб
10 - 2 - 02 Guest Segment Karen Hsu Datameer.mp4 12.93Мб
1 - 10 - Logistics (742).mp4 10.90Мб
1 - 10 - Logistics (742).srt 12.16Кб
1 - 11 - Twitter Assignment Getting Started with Problem 0 and Problem 1.mp4 25.30Мб
1 - 1 - Appetite Whetting Part 1 (1538).mp4 23.90Мб
1 - 1 - Appetite Whetting Part 1 (1538).srt 22.32Кб
1 - 2 - Appetite Whetting Part 2 (1344).mp4 20.23Мб
1 - 2 - Appetite Whetting Part 2 (1344).srt 18.06Кб
1 - 3 - Context (930).mp4 13.41Мб
1 - 3 - Context (930).srt 15.64Кб
1 - 4 - Dimensions (1024).mp4 12.77Мб
1 - 4 - Dimensions (1024).srt 16.52Кб
1 - 5 - This Course Part 1 (1402).mp4 19.68Мб
1 - 5 - This Course Part 1 (1402).srt 22.48Кб
1 - 6 - This Course Part 2 (1050).mp4 14.09Мб
1 - 6 - This Course Part 2 (1050).srt 16.45Кб
1 - 7 - eScience (1146).mp4 16.75Мб
1 - 7 - eScience (1146).srt 19.26Кб
1 - 8 - Big Data (1436).mp4 21.86Мб
1 - 8 - Big Data (1436).srt 24.88Кб
1 - 9 - Guest Lecture Biomedical Informatics (1024).mp4 17.23Мб
1 - 9 - Guest Lecture Biomedical Informatics (1024).srt 12.97Кб
2_instructions.pdf 195.44Кб
2 - 10 - Declarative Languages (1030).mp4 15.28Мб
2 - 10 - Declarative Languages (1030).srt 14.06Кб
2 - 11 - Logical Data Independence (1123).mp4 14.02Мб
2 - 11 - Logical Data Independence (1123).srt 14.65Кб
2 - 1 - From Data Models to Databases (1035).mp4 14.89Мб
2 - 1 - From Data Models to Databases (1035).srt 17.80Кб
2 - 2 - Motivating Relational Algebra (857).mp4 12.66Мб
2 - 2 - Motivating Relational Algebra (857).srt 13.35Кб
2 - 3 - Relational Algebra Introduction (1058).mp4 15.77Мб
2 - 3 - Relational Algebra Introduction (1058).srt 16.84Кб
2 - 4 - Relational Algebra Details Union Diff Select (1053).mp4 15.86Мб
2 - 4 - Relational Algebra Details Union Diff Select (1053).srt 15.37Кб
2 - 5 - Relational Algebra Details Project Cross Product Equi-Join (1106).mp4 16.07Мб
2 - 5 - Relational Algebra Details Project Cross Product Equi-Join (1106).srt 15.22Кб
2 - 6 - Relational Algebra Details Theta-Join (834).mp4 12.20Мб
2 - 6 - Relational Algebra Details Theta-Join (834).srt 12.32Кб
2 - 7 - SQL for Data Science Interpreting Complicated SQL (1212).mp4 18.23Мб
2 - 7 - SQL for Data Science Interpreting Complicated SQL (1212).srt 18.60Кб
2 - 8 - SQL for Data Science User-Defined Functions (759).mp4 11.16Мб
2 - 8 - SQL for Data Science User-Defined Functions (759).srt 11.12Кб
2 - 9 - Physical Optimization (1114).mp4 16.04Мб
2 - 9 - Physical Optimization (1114).srt 16.54Кб
3_js_quiz.pdf 255.42Кб
3_python_instructions.pdf 142.29Кб
3 - 10 - Comparing MapReduce and Databases (0639).mp4 8.95Мб
3 - 10 - Comparing MapReduce and Databases (0639).srt 11.06Кб
3 - 11 - Experimental Results MR and DB (1501).mp4 20.16Мб
3 - 11 - Experimental Results MR and DB (1501).srt 19.72Кб
3 - 1 - Scalability Basics (1618).mp4 23.26Мб
3 - 1 - Scalability Basics (1618).srt 23.52Кб
3 - 2 - Parallel Processing Patterns (1126).mp4 16.39Мб
3 - 2 - Parallel Processing Patterns (1126).srt 16.34Кб
3 - 3 - MapReduce Abstractions (1117).mp4 16.86Мб
3 - 3 - MapReduce Abstractions (1117).srt 16.93Кб
3 - 4 - MapReduce Pseudocode (754).mp4 12.28Мб
3 - 4 - MapReduce Pseudocode (754).srt 10.26Кб
3 - 5 - MapReduce Text Examples (958).mp4 16.16Мб
3 - 5 - MapReduce Text Examples (958).srt 12.98Кб
3 - 6 - MapReduce Relational Join Social Example (1317).mp4 18.74Мб
3 - 6 - MapReduce Relational Join Social Example (1317).srt 17.75Кб
3 - 7 - MapReduce Matrix Multiply Example (931).mp4 13.20Мб
3 - 7 - MapReduce Matrix Multiply Example (931).srt 10.01Кб
3 - 8 - MapReduce Implementation Overview (1338).mp4 18.03Мб
3 - 8 - MapReduce Implementation Overview (1338).srt 19.71Кб
3 - 9 - Parallel Databases (1618).mp4 22.69Мб
3 - 9 - Parallel Databases (1618).srt 24.28Кб
4_aws-setup.pdf 270.41Кб
4_quiz.pdf 257.76Кб
4 - 1 - Statistics Intro (1036).mp4 16.87Мб
4 - 1 - Statistics Intro (1036).srt 15.54Кб
4 - 2 - Publication Bias (845).mp4 13.01Мб
4 - 2 - Publication Bias (845).srt 12.10Кб
4 - 3 - Effect Size Meta-analysis Heteroskedasticity (931).mp4 14.14Мб
4 - 3 - Effect Size Meta-analysis Heteroskedasticity (931).srt 14.23Кб
4 - 4 - Fraud and Benfords Law (1055).mp4 15.98Мб
4 - 4 - Fraud and Benfords Law (1055).srt 14.29Кб
4 - 5 - Multiple Hypothesis Testing (1122).mp4 16.53Мб
4 - 5 - Multiple Hypothesis Testing (1122).srt 15.51Кб
4 - 6 - Recap and Big Data (1139).mp4 17.50Мб
4 - 6 - Recap and Big Data (1139).srt 16.56Кб
4 - 7 - Bayesian Intro (759).mp4 12.51Мб
4 - 7 - Bayesian Intro (759).srt 10.81Кб
4 - 8 - Bayes Rule (1153).mp4 17.67Мб
4 - 8 - Bayes Rule (1153).srt 15.62Кб
5 - 10 - 10 Ensembles Bagging and Boosting (0919).mp4 13.83Мб
5 - 10 - 10 Ensembles Bagging and Boosting (0919).srt 13.31Кб
5 - 1 - 01 Introduction to Machine Learning Part 1 (0754).mp4 11.64Мб
5 - 1 - 01 Introduction to Machine Learning Part 1 (0754).srt 12.31Кб
5 - 11 - 11 Random Forests (1116).mp4 16.79Мб
5 - 11 - 11 Random Forests (1116).srt 16.07Кб
5 - 12 - 12 k Nearest Neighbors (1143).mp4 16.85Мб
5 - 12 - 12 k Nearest Neighbors (1143).srt 15.66Кб
5 - 2 - 02 Introduction to Machine Learning Part 2 (0523).mp4 8.03Мб
5 - 2 - 02 Introduction to Machine Learning Part 2 (0523).srt 8.01Кб
5 - 3 - 03 Rules Part 1 (0919).mp4 12.88Мб
5 - 3 - 03 Rules Part 1 (0919).srt 12.95Кб
5 - 4 - 04 Rules Part 2 (0539).mp4 8.64Мб
5 - 4 - 04 Rules Part 2 (0539).srt 7.49Кб
5 - 5 - 05 Decision Trees Entropy (1051).mp4 15.72Мб
5 - 5 - 05 Decision Trees Entropy (1051).srt 14.15Кб
5 - 6 - 06 Information Gain (1143).mp4 19.25Мб
5 - 6 - 06 Information Gain (1143).srt 17.33Кб
5 - 7 - 07 Overfitting (1104).mp4 16.23Мб
5 - 7 - 07 Overfitting (1104).srt 16.63Кб
5 - 8 - 08 Evaluation and Cross-Validation (1046).mp4 15.41Мб
5 - 8 - 08 Evaluation and Cross-Validation (1046).srt 15.06Кб
5 - 9 - 09 The Bootstrap (0413).mp4 6.64Мб
5 - 9 - 09 The Bootstrap (0413).srt 5.94Кб
6_VisualizationAssignment.twbx 6.04Мб
6 - 10 - 10 Evaluation (325).mp4 5.04Мб
6 - 10 - 10 Evaluation (325).srt 4.18Кб
6 - 1 - 01 Introduction (717).mp4 10.22Мб
6 - 1 - 01 Introduction (717).srt 9.86Кб
6 - 2 - 02 Data Types (937).mp4 13.46Мб
6 - 2 - 02 Data Types (937).srt 12.63Кб
6 - 3 - 03 Data Types (Exercises) (407).mp4 5.95Мб
6 - 3 - 03 Data Types (Exercises) (407).srt 4.90Кб
6 - 4 - 04 Data Dimensions (308).mp4 4.36Мб
6 - 4 - 04 Data Dimensions (308).srt 3.90Кб
6 - 5 - 05 Visual Encoding (Part 1) (638).mp4 9.61Мб
6 - 5 - 05 Visual Encoding (Part 1) (638).srt 7.81Кб
6 - 6 - 06 Visual Encoding (Part 2) (254).mp4 4.23Мб
6 - 6 - 06 Visual Encoding (Part 2) (254).srt 3.54Кб
6 - 7 - 07 Visual Perception (Part 1) (439).mp4 6.54Мб
6 - 7 - 07 Visual Perception (Part 1) (439).srt 6.23Кб
6 - 8 - 08 Visual Perception (Part 2) (418).mp4 5.99Мб
6 - 8 - 08 Visual Perception (Part 2) (418).srt 5.23Кб
6 - 9 - 09 Visual Perception (Part 3) (356).mp4 5.31Мб
6 - 9 - 09 Visual Perception (Part 3) (356).srt 4.41Кб
7 - 10 - 10 Optimizing MapReduce for Graph Traversal (0820).mp4 12.16Мб
7 - 1 - 01 Graph Basics (0622).mp4 8.74Мб
7 - 11 - 11 Graph Representations (0651).mp4 9.66Мб
7 - 12 - 12 PageRank in MapReduce and Pregel (1042).mp4 15.04Мб
7 - 2 - 02 Structure Degree Histograms (0814).mp4 11.81Мб
7 - 3 - 03 Structure Diameter Connectivity Centrality (0656).mp4 9.62Мб
7 - 4 - 04 Traversal PageRank (0637).mp4 9.39Мб
7 - 5 - 05 Traversal Spanning Trees Circuits Flows (0657).mp4 10.30Мб
7 - 6 - 06 Patterns Triangles SPARQL Datalog (1002).mp4 13.90Мб
7 - 7 - 07 Patterns Relational Algebra for Graph Query (0826).mp4 11.97Мб
7 - 8 - 08 PRISM Example in Datalog (1110).mp4 16.20Мб
7 - 9 - 09 Evaluating Graph Traversal Queries (0829).mp4 11.59Мб
8 - 10 - Pig Functions (1141).mp4 16.48Мб
8 - 10 - Pig Functions (1141).srt 15.90Кб
8 - 11 - Pig Join and Co-Group Join (1610).mp4 22.43Мб
8 - 11 - Pig Join and Co-Group Join (1610).srt 22.69Кб
8 - 12 - Pig Evaluation (1011).mp4 14.38Мб
8 - 12 - Pig Evaluation (1011).srt 14.99Кб
8 - 1 - NoSQL Introduction (830).mp4 12.79Мб
8 - 1 - NoSQL Introduction (830).srt 13.18Кб
8 - 2 - Eventual Consistency (1856).mp4 25.95Мб
8 - 2 - Eventual Consistency (1856).srt 28.59Кб
8 - 3 - Example Memcached (1637).mp4 23.31Мб
8 - 3 - Example Memcached (1637).srt 23.38Кб
8 - 4 - Example Dynamo (1016).mp4 13.78Мб
8 - 4 - Example Dynamo (1016).srt 13.62Кб
8 - 5 - Example CouchDB (1000).mp4 14.04Мб
8 - 5 - Example CouchDB (1000).srt 15.15Кб
8 - 6 - Example BigTable (1105).mp4 15.49Мб
8 - 6 - Example BigTable (1105).srt 15.56Кб
8 - 7 - Example Other Google Systems (1618).mp4 23.73Мб
8 - 7 - Example Other Google Systems (1618).srt 24.67Кб
8 - 8 - Response to NoSQL Systems (1444).mp4 19.37Мб
8 - 8 - Response to NoSQL Systems (1444).srt 22.05Кб
8 - 9 - Pig Intro (1202).mp4 15.37Мб
8 - 9 - Pig Intro (1202).srt 18.05Кб
9 - 1 - 01 Gradient Descent Part 1 (0718).mp4 10.14Мб
9 - 1 - 01 Gradient Descent Part 1 (0718).srt 11.27Кб
9 - 2 - 02 Gradient Descent Part 2 (0629).mp4 9.07Мб
9 - 2 - 02 Gradient Descent Part 2 (0629).srt 9.05Кб
9 - 3 - 03 Intuition for Logistic Regression and Support Vector Machines (1055).mp4 15.54Мб
9 - 3 - 03 Intuition for Logistic Regression and Support Vector Machines (1055).srt 15.59Кб
9 - 4 - 04 Intuition for Regularization (0659).mp4 9.90Мб
9 - 4 - 04 Intuition for Regularization (0659).srt 10.39Кб
9 - 5 - 05 Stochastic Gradient Descent Minibatches Parallelization (0853).mp4 13.01Мб
9 - 5 - 05 Stochastic Gradient Descent Minibatches Parallelization (0853).srt 13.22Кб
9 - 6 - 06 Unsupervised Learning (0611).mp4 8.69Мб
9 - 6 - 06 Unsupervised Learning (0611).srt 10.18Кб
9 - 7 - 07 K-means (0556).mp4 7.97Мб
9 - 7 - 07 K-means (0556).srt 7.30Кб
9 - 8 - 08 DBSCAN (0913).mp4 13.71Мб
9 - 8 - 08 DBSCAN (0913).srt 13.03Кб
Class Virtual Machine.pdf 88.58Кб
Course Logistics.pdf 123.80Кб
Coursera-Data-Science-Ubuntu.ova 2.34Гб
Github Instructions.pdf 127.83Кб
guests-datameer_beyond_mapreduce.pdf 188.82Кб
guests-kiji-uw-data-science.pdf 736.07Кб
Infovis Aragon 10 Evaluation.pdf 298.38Кб
Infovis Aragon 1 Introduction.pdf 673.51Кб
Infovis Aragon 2 Data Types.pdf 954.69Кб
Infovis Aragon 3 Data Types (Exercises).pdf 424.06Кб
Infovis Aragon 4 Data Dimensions.pdf 670.77Кб
Infovis Aragon 5 Visual Encoding (Part 1).pdf 750.04Кб
Infovis Aragon 6 Visual Encoding (Part 2).pdf 685.36Кб
Infovis Aragon 7 Visual Perception (Part 1).pdf 884.68Кб
Infovis Aragon 8 Visual Perception (Part 2).pdf 449.81Кб
Infovis Aragon 9 Visual Perception (Part 3).pdf 330.23Кб
Introduction to JSMapreduce.mp4 48.88Мб
Python Resources.pdf 83.55Кб
Running Tableau on AWS.pdf 874.43Кб
Syllabus.pdf 202.87Кб
TableauDesktop.exe 87.36Мб
tips.txt 856б
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